Many proteins are present at low concentrations in biological samples, and therefore, techniques for ultrasensitive protein detection are necessary. To overcome challenges with sensitivity, the digital enzyme-linked immunosorbent assay (ELISA) was developed, which is 1000× more sensitive than conventional ELISA and allows sub-femtomolar protein detection. However, this sensitivity is still not sufficient to measure many proteins in various biological samples, thereby limiting our ability to detect and discover biomarkers. To overcome this limitation, we developed droplet digital ELISA (ddELISA), a simple approach for detecting low protein levels using digital ELISA and droplet microfluidics. ddELISA achieves maximal sensitivity by improving the sampling efficiency and counting more target molecules. ddELISA can detect proteins in the low attomolar range and is up to 25-fold more sensitive than digital ELISA using Single Molecule Arrays (Simoa), the current gold standard tool for ultrasensitive protein detection. Using ddELISA, we measured the LINE1/ORF1 protein, a potential cancer biomarker that has not been previously measured in serum. Additionally, due to the simplicity of our device design, ddELISA is promising for point-of-care applications. Thus, ddELISA will facilitate the discovery of biomarkers that have never been measured before for various clinical applications.

Background Type 2 diabetes affects Indigenous and non-European populations disproportionately, including in New Zealand, where long-term temporal trends in cause-specific clinical outcomes between Māori, Pacific, and European people remain unclear. We aimed to compare the rates of mortality and hospital admission between Māori, Pacific, and European patients with type 2 diabetes in Auckland, New Zealand, over a period of 24 years.Methods In this retrospective, population-based, longitudinal cohort study, we identified a cohort of patients (aged 35-84 years) with type 2 diabetes enrolled between Jan 1, 1994, and July 31, 2018, to the primary care audit programme, the Diabetes Care Support Service (DCSS) in Auckland, New Zealand. Patients with type 1 diabetes, prediabetes, and gestational diabetes were excluded. We linked data from the DCSS with national death registration, hospital admission, pharmaceutical claim, and socioeconomic status databases. Patients were followed up until death or July 31, 2018 (date of last enrolment to the DCSS). Incident clinical events (all-cause mortality, cardiovascular mortality, cancer mortality, cardiovascular hospital admission, cancer hospital admission, and end-stage renal disease hospital admission) were identified. Event rates were stratified by ethnic group, age group, sex, socioeconomic status, and time period (<1998, 1999-2013, 2004-08, 2009-13, and 2014-18). Incidence rate ratios (IRRs) and absolute risk differences were adjusted for sex, age, smoking status, obesity, socioeconomic status, and time period by use of age-period-cohort modelling.

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Two prediction models that quantify risks of cardiovascular hospitalization and rehospitalization have been developed and externally validated. They are based on a small number of clinical measurements that are available for patients with type 2 diabetes in many developed countries in primary care settings and could serve as the tools to screen the population at high risk of cardiovascular hospitalization and rehospitalization.

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Purpose This study examined the association between remnant cholesterol (remnant-C) and cardiovascular mortality in patients with type 2 diabetes (T2D), chronic kidney disease (CKD) stages 3-5 and newly diagnosed diabetic nephropathy (DN). Methods This study determined the baseline lipid profile and searched for deaths with cardiovascular disease (CVD) within 2 years of baseline among 2282 adults enrolled between 01/01/2015 and 31/12/2016 who had T2D, CKD stages 3-5 and newly diagnosed DN. Adjusted Logistic regression models were used to assess the associations between lipid, especially remnant-C concentration (either as continuous or categorical variables), and risk of cardiovascular mortality. Results In multivariable-adjusted analyses, low-density lipoprotein cholesterol (LDL-C) (odds ratio [OR]:1.022; 95% confidence interval [CI]: 1.017-1.026, per 10mg/dl), HDL-C (0.929 [0.922-0.936], per 5 mg/dl), Non-HDL-C (1.024 [1.021-1.028], per 10mg/dl), and remnant-C (1.115 [1.103-1.127], per 10mg/dl), but not triglyceride were associated with cardiovascular mortality. Atherogenic dyslipidemia (triglycerides &gt;150 mg/dl [1.69 mmol/l] and HDL-C &lt;40 mg/dl in men or &lt;50 mg/dl in women) was also associated with cardiovascular mortality (1.073[1.031-1.116]). Remnant-C ≥30 mg/dl differentiated patients at a higher risk of cardiovascular mortality compared with those with lower concentrations, especially with interaction with LDL-C level &gt;100 mg/dl: the highest risk was found in patients with higher levels of both remnant-C and LDL-C (1.696 [1.613-1.783]). Conclusions In patients with T2D, CKD stages 3-5 and incident DN, remnant-C was associated with higher risk of death with CVD. Different from the general population, the interaction of remnant-C and LDL-C was associated with the highest risk of cardiovascular mortality.

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